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12th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021

作者： Gamage, Rashmika Rajapaksa, Hasitha Sangeeth, Abhiman Hemachandra, Gimhani Wijekoon, Janaka Nawinna, Dasuni Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe 10115 Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe 10115 Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe 10115 Sri Lanka

ISBN: (纸本)9781665400664

Agriculture planning plays a dominant role in the economic growth and food security of agriculture-based countries such as Sri Lanka. Even though agriculture plays a vital role, there are still several major complications to be addressed. Some of the major complications are lack of knowledge about yield and price resulting in the farmers selecting crops based on experience. Machine learning has a great potential to solve these complications. To this end, this paper proposes a novel system comprises of a mobile application, SMS (Short Message Service), and API (Application Programming Interface) with yield prediction, price prediction, and crop optimization. Several machine learning algorithms were used for yield and price predictions while a generic algorithm was used to optimize crops. The yield was predicted considering the environmental factors while the price was predicted considering supply and demand, import and export, and seasonal effect. To select the best suitable crops to cultivate, the output of yield and price prediction have been used. Yield prediction has been implemented using elastic net, ridge, and multilinear regression. R2 of yield prediction is varied from 0.74 to 0.89 while RMSE value is between 15.69 and 35.05. Price prediction has been implemented using the algorithms of Gradient Boosting Tree, Random Forest, Facebook Prophet, and R2 is varied from 0.72 to 0.92 while RMSE value is between 26.81 and 140.72. Crop optimization has been implemented using the genetic algorithm. © 2021 IEEE.

关键词： Crops

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OctopusNet: Machine learning for intelligent management of surgical tools

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Smart Health 2022年 23卷

作者： Rodrigues, Mark Mayo, Michael Patros, Panos Department of Computer Science University of Waikato Hamilton New Zealand Department of Software Engineering University of Waikato Hamilton New Zealand

This paper presents a novel densely-connected convolutional neural network, termed OctopusNet, for hierarchical classification of surgical tools. The network shares information across prediction hierarchies to improve classification accuracy, and provides a degree of interpretability by predicting a set of features for each tool based on multiple classification targets. Important contributions of our work are the OctopusNet architecture, a novel surgical tool dataset and a surgery knowledge base. The architecture provides a viable solution for intelligent management of surgical tools in a hospital, potentially leading to greater patient safety, significant cost savings and increased efficiencies. © 2021 Elsevier Inc.

关键词： Hierarchical classification Message propagation OctopusNet Surgery knowledge base Surgical tool dataset

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Surface Recognition from Wheelchair-induced Noisy Vibration Data: A Tale of Many Cities

Surface Recognition from Wheelchair-induced Noisy Vibration ...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Rochishnu Banerjee Md Fourkanul Islam Shaswati Saha Vaskar Raychoudhury Md Osman Gani Department of Computer Science & Software Engineering Miami University Oxford OH USA Department of Information Systems University of Maryland Baltimore County Baltimore MD USA

Despite the active legislation in many countries supporting the accessibility of public spaces by mobility-impaired users, the reality is far from ideal. Wheelchair users often struggle to navigate the built environment let alone the natural areas. While barriers to wheeled mobility can be caused by broken/uneven surfaces, steep slopes, and unfavorable weather conditions, the effects of many such factors and others are not properly investigated. In this paper, we aim to classify various built and natural surfaces through their characteristic vibration patterns using different deep learning algorithms. The surface vibration data is collected from various cities in Europe (including Paris (FR), Mannheim (DE), Dresden (DE), Munich, Nuremberg (DE), and Salzburg (AT)) while a user drives a manual wheelchair attached with three differently oriented smartphones placed at different heights. Extensive experiments show that a Deep Neural Network model classifies surfaces using a denoised dataset with a 98.9% accuracy which is significantly higher than our previous state-of-the-art.

关键词： Vibrations Deep learning Navigation Wheelchairs Urban areas Neural networks Sensors

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BeSafe: Air Quality Monitoring and Control with IoT

BeSafe: Air Quality Monitoring and Control with IoT

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： K.C Arun Sadia Azam Ayesha Siddique Mubashir Ali Aamir Hussain School of Computing Asia Pacific University Kuala Lumpur Malaysia Dipartimento di Informatica Universita di Verona San Francesco Italy Department of Computer Science University of Agriculture Faisalabad Pakistan Department of Software Engineering Lahore Garrison University Lahore Pakistan Department of Computer Science Muhammad Nawaz Shareef University of Agriculture Multan Pakistan

Air pollution is a common matter nowadays. It’s defined as contamination of physical, biological, or chemical alteration in the air which results in harmful effects on living objects and makes it difficult for them to survive. The prominent cause of air pollution is the discharge of injurious gases from industrial waste and the use of fossil fuels. The effects of air pollution are very severe climate change, soil erosion, different diseases, and many more thus it’s very important to take proper note of air superiority as long-term effects of air pollution can result in heart diseases, lung cancer, etc. We have devised a system to measure the contamination level of air through sensors base hardware which sends the data directly to the associated server making it easier to read even on mobile "through a software application. The sensors would be plotted in different routes and locations i.e., industrial region, traffic zone, etc." to investigate the pollution stages of the air making it easier for the user to take a safer route. One of the important usages of the system is to give information about the low contaminated areas near roads and industrial areas so that they can be safeguarded through certain measures.

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Automatic Generation of Individual Fuzzy Cognitive Maps from Longitudinal Data

arXiv

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arXiv 2022年

作者： Wozniak, Maciej K. Mkhitaryan, Samvel Giabbanelli, Philippe J. Department of Computer Science & Software Engineering Miami University OxfordOH45056 United States Department of Health Promotion CAPHRI Maastricht University Maastricht Netherlands

Fuzzy Cognitive Maps (FCMs) are computational models that represent how factors (nodes) change over discrete interactions based on causal impacts (weighted directed edges) from other factors. This approach has traditionally been used as an aggregate, similarly to System Dynamics, to depict the functioning of a system. There has been a growing interest in taking this aggregate approach at the individual-level, for example by equipping each agent of an Agent-Based Model with its own FCM to express its behavior. Although frameworks and studies have already taken this approach, an ongoing limitation has been the difficulty of creating as many FCMs as there are individuals. Indeed, current studies have been able to create agents whose traits are different, but whose decision-making modules are often identical, thus limiting the behavioral heterogeneity of the simulated population. In this paper, we address this limitation by using Genetic Algorithms to create one FCM for each agent, thus providing the means to automatically create a virtual population with heterogeneous behaviors. Our algorithm builds on prior work from Stach and colleagues by introducing additional constraints into the process and applying it over longitudinal, individual-level data. A case study from a real-world intervention on nutrition confirms that our approach can generate heterogeneous agents that closely follow the trajectories of their real-world human counterparts. Future works include technical improvements such as lowering the computational time of the approach, or case studies in computational intelligence that use our virtual populations to test new behavior change interventions. © 2022, CC BY-SA.

关键词： Genetic algorithms

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

A Deep Learning Approach Towards Student Performance Predict...

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Information Technology (ACIT)

作者： Abdallah Moubayed MohammadNoor Injadat Nouh Alhindawi Ghassan Samara Sara Abuasal Raed Alazaidah School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Electrical and Computer Engineering Department Western University London Ontario Canada Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset.

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A Transfer Learning Based Detection and Grading of Cataract using Fundus Images

A Transfer Learning Based Detection and Grading of Cataract ...

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IEEE International Conference on Multi Topic

作者： Ahsan Abbas Abdulkareem Alzahrani Azhar Imran Abdullah Almuhaimeed Ali Haider Khan Department of Creative Technologies Air University Islamabad Pakistan Faculty of Computer Science & IT Al-Baha University Saudi Arabia Digital Health Institute King Abdulaziz for Science and Technology Riyadh Saudi Arabia Department of Software Engineering Lahore Garrison University Lahore Pakistan

Among the most prominent causes for visual impairment, especially among older adults, is cataract. As per the World Health Organisation (WHO), there are 2.2 billion individuals globally are estimated to be blind or possess an eyesight problem. One of the most identified and important causes of this is cataracts. Cataracts should be identified and treated as soon as possible to avoid blindness. Ophthalmologists use an expensive slit lamp to diagnose cataracts in regions with few medical facilities. Consequently, the issue is that a lack of skilled ophthalmologists may delay the identification of cataracts, for which medical treatment is unavoidable. Medical image analysis based on artificial intelligence provides a rapid and precise diagnosis in modern healthcare. We utilized deep learning models based on transfer learning, namely VGG19, and ResNet-50, to diagnose cataracts using fundus images and enhance classification accuracy. The performance metric for the model is accuracy; the highest achieved accuracy was 98%.

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Using Microservice Telemetry Data for System Dynamic Analysis

arXiv

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arXiv 2022年

作者： Al Maruf, Abdullah Bakhtin, Alexander Cerny, Tomas Taibi, Davide Department of Computer Science Baylor University WacoTX United States Software Engineering Group Tampere University TampereFI-33720 Finland

Microservices bring various benefits to software systems. They also bring decentralization and lose coupling across self-contained system parts. Since these systems likely evolve in a decentralized manner, they need to be monitored to identify when possibly poorly designed extensions deteriorate the overall system quality. For monolith systems, such tasks have been commonly addressed through static analysis. However, given the decentralization and possible language diversity across microservices, static analysis tools are lacking. On the other hand, there are available tools commonly used by practitioners that offer centralized logging, tracing, and metric collection for microservices. In this paper, we assess the opportunity to combine current dynamic analysis tools with anomaly detection in the form of quality metrics and anti-patterns. We develop a tool prototype that we use to assess a large microservice system benchmark demonstrating the feasibility and potential of such an approach. © 2022, CC BY.

关键词： Static analysis

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Toward Research software Categories

arXiv

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arXiv 2024年

作者： Hasselbring, Wilhelm Druskat, Stephan Bernoth, Jan Betker, Philine Felderer, Michael Ferenz, Stephan Lamprecht, Anna-Lena Linxweiler, Jan Rumpe, Bernhard Software Engineering Kiel University Kiel24098 Germany Berlin12489 Germany University of Potsdam Potsdam14476 Germany Department for Epidemiology Helmholtz Centre for Infection Research Brunswick Germany University of Cologne Cologne51147 Germany Department of Computer Science Carl von Ossietzky Universität Oldenburg Oldenburg26129 Germany TU Braunschweig Braunschweig38106 Germany Software Engineering RWTH Aachen University Germany

Research software has been categorized in different contexts to serve different goals. We start with a look at what research software is, before we discuss the purpose of research software categories. We propose a multi-dimensional categorization of research software. We present a template for characterizing such categories. As selected dimensions, we present our proposed role-based, developer-based, and maturity-based categories. Since our work has been inspired by various previous efforts to categorize research software, we discuss them as related works. We characterize all these categories via the previously introduced template, to enable a systematic comparison. Copyright © 2024, The Authors. All rights reserved.

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Modelling distillate output of a solar distiller with eco-friendly wood-based steam generation basin using machine learning model and starling murmuration optimizer

International Journal of Thermofluids

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International Journal of Thermofluids 2025年 27卷

作者： Al-Betar, Mohammed Azmi Muskens, Otto L. Hammoodi, Karrar A. Abd Elaziz, Mohamed Shambour, Qusai Yousef Fraihat, Salam Elsheikh, Ammar H. College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Faculty of Engineering and Physical Sciences University of Southampton Southampton SO17 1BJ United Kingdom College of Engineering University of Al Maarif Al Anbar31001 Iraq Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Software Engineering Department Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Production Engineering and Mechanical Design Faculty of Engineering Tanta University Tanta31527 Egypt Faculty of Engineering Pharos University in Alexandria Egypt

Yield augmentation of solar distiller (SD) using low-cost modifications with minimum environmental impacts is highly desirable. In this study, we developed an eco-friendly steam generation device made of carbonized Ficus sycomorus wood to boost the yield of the conventional single-basin solar distiller. Moreover, a hybrid model composed of a long short-term memory (LSTM) network and a Starling murmuration optimizer (SMO) was developed to predict the yield of the established SD. To verify the accuracy of the model, three other models were employed and the distillate yield was predicted. The predicted data of all models was evaluated using different error measures. These models were standalone LSTM and LSTM optimized by an artificial hummingbird algorithm (AHA) or manta ray foraging optimizer (MRFO). Real field data were employed during the training and testing of all models. The thermal performance of the distiller with the steam generation device was compared with that of a traditional distiller based on exergy output, exergy efficiency, and energy efficiency. The distillate yield, exergy efficiency, and energy efficiency of the modified SD were enhanced by 34 %, 49 %, and 40 %, respectively, compared with that of the traditional distiller. Moreover, the total amount of produced drinkable water using the modified SD could reach a high value of 6.1 L/day with a low cost of 0.014 $/L. © 2025

关键词： Exergy

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